Reciprocity inspired learning for opportunistic spectrum access in cognitive radio networks

This paper addresses opportunistic spectrum access (OSA) in non-cooperative cognitive radio networks (CRNs). The selfish behaviors of the secondary users (SUs) will cause a CRN to collapse. The SUs are thus enabled to build beliefs about how other SUs would respond to their decision makings. The interaction among the SUs is modeled as a stochastic learning process. In this way, each SU can independently learn the behaviors of the competitors, optimize the OSA strategies, and finally achieve the goal of reciprocity. Two learning algorithms are proposed to stabilize the stochastic CRNs, the convergence properties of which are also proven theoretically. Simulation results validate the performance of the proposed results, and show that the achieved system performance outperforms some existing protocols.